[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-617825-105":59,"doc-detail-617825-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","from-words-to-themes-ai-powered-qualitative-data-coding-and-analysis","From Words to Themes - AI-Powered Qualitative Data Coding and Analysis","","This article presents an AI-driven qualitative research methodology for coding interview transcripts and conducting thematic analysis using advanced linguistic models. Two inductive strategies are introduced—generative coding and lexico-semantic coding—both grounded in bottom-up logic and supported by tools such as ChatGPT and NLP techniques. The approach automates codebook or dictionary structure building, improving precision, efficiency, fit to linguistic data, effectiveness, and interpretability. It also outlines a semi-supervised framework to automate routine coding while enabling researchers to focus on interpretation, theorising, and digital qualitative 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qualitative coding and analysis approach does the article propose?","Question",{"text":111,"@type":112},"It proposes an AI-powered qualitative methodology that automates inductive coding of interview transcriptions and supports thematic analysis using linguistic models.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How do the two inductive coding strategies differ?",{"text":116,"@type":112},"The article describes two strategies: generative coding and lexico-semantic coding, both using bottom-up logic to build the codebook structure from language data.",{"name":118,"@type":109,"acceptedAnswer":119},"What methodological challenges does the article discuss when applying AI to qualitative analysis?",{"text":120,"@type":112},"It highlights reliability and validation issues and stresses the need for a critical stance regarding the researcher’s role in developing and using new 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methodology that usesadvanced artificial intelligence-based linguistic models in free-text interview codingand thematic analysis.The authors describe two strategies for inductive codingof interview transcriptions:generative coding and lexico-semantic coding.Bothapproaches use bottom-up logic,employing language models like ChatGPT andnatural language processing techniques to automate building the codebook struc-ture.This innovative coding technique enhances the precision and efficiency of qual-itative data analysis and reasoning within thematic analysis.On the other hand,itimproves the fit of the analytical model (the structure of the codebook or dictio-nary)to the linguistic data of the interviews,thereby increasing its effectivenessand interpretability.The article presents a semi-supervised qualitative data codingmethodology using artificial intelligence algorithms.This method can potentiallyautomate routine qualitative data coding procedures in computer-assisted data anal-ysis,freeing the qualitative researcher's time to focus on interpretation and theorising.Additionally,it is a step towards the development of digital qualitative sociology.  \nKeywords Inductive coding·Thematic analysis·Codebook development·Natural language processing(NLP)·Large language models (LLM)·ChatGPT·Artificial intelligence(AI)  \nD.Sadowski  \nFaculty of Mathematics and Computer Science,Division of Computational Mathematics,Jagiellonian University,St.Lojasiewicza 6,30-348 Kraków,Poland  \nDoctoral School of Exact and Natural Sciences.Jagiellonian University,St.Lojasiewicza 11,30-348 Kraków,Poland  \nD.Sadowskie-mail:damian,sadowski@doctoral.uj.edu.pl  \n◎ The Author(s),under exclusive license to Springer Nature Switzerland AG 2024  \nJ.Ribeiro et al.(eds.),Computer Supported Qualitative Research,Lecture Notes  \nin Networks and Systems 1061,https://doi.org/10.1007/978-3-031-65735-1_19  \n# 1 Introduction\n\nWith the rapid advancement of new information technologies and progress in artificialintelligence(AI),we witness a significant increase in their application potentialin computerized text analysis.Innovations such as large language models (LLM),natural language processing(NLP)tools,and AI have the potential to revolutionizetraditional computer-assisted qualitative data analysis(CAQDAS)by automating theextraction of frequently occurring phrases and themes.These technologies representa paradigm shift in qualitative data processing,offering unprecedented scalabilityand efficiency.  \nArtificial intelligence enables faster data processing and better categorization byidentifying subtle patterns that could.With the rapid advancement of new informationtechnologies and progress in artificial intelligence,we are witnessing a significantincrease in their application potential in computerized text analysis.Innovationssuch as large language models,natural language processing tools,and AI can revo-lutionize traditional CAQDAS by automating the extraction of frequently occurringphrases and themes.These technologies represent a paradigm shift in qualitativedata processing,offering unprecedented scalability and efficiency.AI enables us toprocess and categorise data faster by identifying subtle patterns that could be over-looked during manual analysis.Integrating AI into qualitative research enhancesthe reliability of results,providing a more systematic approach to data analysis andreducing the risk of human error and bias.Advanced AI algorithms increase theobjectivity of the coding process,minimising the impact of researchers'subjectivebeliefs on data interpretation.  \nIn this context,the article addresses fundamental research problems related toqualitative data analysis,focusing on developing an effective inductive codingmethod using AI.Our goalis to create a well-matched dictionary for analyzing empir-ical data,an","cbCairTe20TaedF0","https://ap.wps.com/l/cbCairTe20TaedF0","pdf",7547715,37,"English","# 1 Introduction\n## AI and computerized text analysis\n## Inductive coding with AI\n## Methodological challenges and research implications","[{\"question\":\"What qualitative coding and analysis approach does the article propose?\",\"answer\":\"It proposes an AI-powered qualitative methodology that automates inductive coding of interview transcriptions and supports thematic analysis using linguistic models.\"},{\"question\":\"How do the two inductive coding strategies differ?\",\"answer\":\"The article describes two strategies: generative coding and lexico-semantic coding, both using bottom-up logic to build the codebook structure from language data.\"},{\"question\":\"What methodological challenges does the article discuss when applying AI to qualitative analysis?\",\"answer\":\"It highlights reliability and validation issues and stresses the need for a critical stance regarding the researcher’s role in developing and using new technologies.\"}]","From Words to Themes - AI-Powered Qualitative Data Coding and Analysis | PDF",93]